Neck circumference as a predictor of metabolic syndrome, insulin resistance and low-grade systemic inflammation in children: The ACFIES study

The current study aims to evaluate the association between neck circumference (NC) and several cardio-metabolic risk factors, to compare it with well-established anthropometric indices, and to determine the cut-off point value of NC for predicting children at increased risk of metabolic syndrome, insulin resistance and low-grade systemic inflammation.

R E S E A R C H A R T I C L E Open Access

Neck circumference as a predictor of

metabolic syndrome, insulin resistance and

low-grade systemic inflammation in

children: the ACFIES study

Diego Gomez-Arbelaez1,2,3 , Paul Anthony Camacho1, Daniel Dylan Cohen1,2, Sandra Saavedra-Cortes2,

Cristina Lopez-Lopez4and Patricio Lopez-Jaramillo1,2*

Abstract

Background: The current study aims to evaluate the association between neck circumference (NC) and several

cardio-metabolic risk factors, to compare it with well-established anthropometric indices, and to determine the

cut-off point value of NC for predicting children at increased risk of metabolic syndrome, insulin resistance and

low-grade systemic inflammation

Methods: A total of 669 school children, aged 8–14, were recruited Demographic, clinical, anthropometric and biochemical data from all patients were collected Correlations between cardio-metabolic risk factors and NC and other anthropometric variables were evaluated using the Spearman’s correlation coefficient Multiple linear regression analysis was applied to further examine these associations We then determined by receiver operating characteristic (ROC) analyses the optimal cut-off for NC for identifying children with elevated cardio-metabolic risk

Results: NC was positively associated with fasting plasma glucose and triglycerides (p = 0.001 for all), and systolic and diastolic blood pressure, C-reactive protein, insulin and HOMA-IR (p < 0.001 for all), and negatively with HDL-C (p = 0.001) Whereas, other anthropometric indices were associated with fewer risk factors

Conclusions: NC could be used as clinically relevant and easy to implement indicator of cardio-metabolic risk in children Keywords: Childhood obesity, Anthropometric measurements, Neck circumference, Metabolic syndrome, Low-grade systemic inflammation, Insulin resistance, Cardiometabolic risk, Latin America, Colombia

Background

The prevalence of obesity in children and adolescents is

increasing worldwide and it is now recognized as an

international public health concern [1] Epidemiological

and clinical investigations have revealed that the

associ-ation between obesity and cardiovascular and metabolic

risk factors begins early in life [2, 3] Childhood obesity

is associated with increased prevalence of hypertension,

dyslipidemia, and abnormal glucose tolerance [2–4]

Thus, identifying and controlling childhood obesity is an

important goal in the prevention of cardiovascular diseases (CVD) in later life [5]

Although obesity is at the core of the development of CVD, appropriate anthropometric measures and cut-off points to identify children with elevated cardio-metabolic risk factors are not well established The most widely used method to categorize overweight and obese children and to predict cardiovascular and metabolic risk is the body mass index (BMI) [6] However, BMI has been considered as an imperfect measure of adiposity, because it does not distin-guish between muscle mass and fat mass, and requires calculations and the use of charts that may not always be available [7, 8]

Alternative measures to BMI such as waist-to-hip ratio (WHR) and waist circumference, which also give some

* Correspondence: jplopezj@gmail.com

1

Dirección de Investigaciones, Fundación Oftalmológica de Santander

-FOSCAL, Floridablanca, Colombia

2 Instituto MASIRA, Facultad de la Ciencias de la Salud, Universidad de

Santander - UDES, Bucaramanga, Colombia

Full list of author information is available at the end of the article

© 2016 Gomez-Arbelaez et al Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made The Creative Commons Public Domain Dedication waiver

indication of fat distribution, have been used as

alterna-tives, but none of these have been accepted as a gold

standard measure to identify cardiovascular and

meta-bolic risk [9, 10] Both have limitations in distinguishing

the contribution from ectopic adipose tissue and

sub-cutaneous adipose tissue [11], which show strong and

modest correlations to cardio-metabolic risk,

respect-ively [12, 13]

Prior studies have suggested that upper body fat

plays a role in cardio-metabolic risk [14, 15], and neck

circumference (NC) was proposed as a new

measure-ment to evaluate overweight and obesity in children

[16–18] NC has demonstrated to be an independent

predictor of metabolic risk beyond BMI and waist

cir-cumference [15] and to be positively associated with

insulin resistance and visceral adipose tissue in adults

[19], but few studies have been conducted to

deter-mine its association with cardio-metabolic risk factors

in children [20, 21] Hence, the aims of the present

study were to evaluate the association between NC

and several cardio-metabolic risk factors and to compare

these associations with those of BMI and other

well-established anthropometric indexes in a Latin American

pediatric population

Methods

Study population

During the 2011–2012 school year, we conducted the

cross-sectional component of the ACFIES study

(Associ-ation between Cardiorespiratory Fitness, Muscular Strength

and Body Composition with Metabolic Risk Factors

in Colombian Children) to identify the prevalence

and associations of cardiovascular risk factors, in a sample

of schoolchildren from both sexes, enrolled in public

elementary and high schools (grades 5 and 6), from the

city of Bucaramanga, Colombia All the recruited

partici-pants met the general ACFIES inclusion criteria: age range

8 to 14 years, not having any physical disability and be free

of any acute infection lasting less than 2 weeks before the

inclusion Moreover, children were excluded if were using

medications that could alter blood pressure, insulin

resist-ance, glycemic levels and/or lipid profile The study

proto-col was in accordance with the Declaration of Helsinki

and was approved by the Health Research Ethics Board of

the Ophthalmological Foundation of Santander

(FOS-CAL) The children expressed their interest in

partici-pating in the study, and parents or legal guardians gave

written informed consent, before the children were

included in the study

Anthropometric measurements and physical examination

All physical assessments and anthropometric

measure-ments were performed after an overnight fast (8 to 10 h), in

duplicate by well-trained health workers For the analysis

we used the mean of the two measurements Participant’s body weight was measured to the nearest 0.1 kg on an elec-tronic device (Tanita BC544, Tokyo, Japan), in underwear and without shoes, and height was measured to the nearest 0.1 cm using a mechanical stadiometer with platform (Seca

274, Hamburg, Germany), while participants were asked to stand erect with their head positioned in the Frankfort horizontal plane BMI was calculated by dividing body weight by the square of height (BMI = weight (kg)/ height (m)2) The weight status was classified accord-ing to Barlow et al [22]

Neck circumference was measured to the nearest 0.1 cm using a tape measure The superior border of the tape measure was placed just below the laryngeal prom-inence and applied perpendicular to long axis of the neck Waist circumference was determined at the middle point between the lower edge of the ribs and the iliac anterior spine The measurement was made at the end

of a normal expiration while the subject stood upright Hip circumference was measured over non-restrictive underwear at the level of the maximum extension of the buttocks posteriorly in a horizontal plane All circumfer-ences were measured using a measuring tape with spring scale (Ohaus 8004-MA, NJ, USA) WHR was calculated

as waist circumference divided by hip circumference Waist-to-height ratio (WHtR) was calculated by dividing waist circumference by height in cm The measurements were realized according to the procedures previously de-scribed by Lohman et al [23]

Skinfold thickness was measured to the nearest 0.2 mm

on the right side of the body at the triceps and subscapular sites using a skinfold caliper (Harpenden C-136, United Kingdom) and body fat percentage (%BF-Skinfold) esti-mated using skinfold equations described by Slaughter et al [24] Body fat percentage was also assessed by bioelectrical impedance analysis (BIA) (%BF-BIA) (Tanita BC544, Tokyo, Japan) Systolic blood pressure and diastolic blood pressure were determined after a resting period of 10 min in the sitting position using an automatic and calibrated sphygmo-manometer with a pediatric cuff (Omron HEM 757 CAN, Hoofddorp, Netherlands) Pubertal development was assessed by Tanner stage of breast development in girls and testicular volume in boys [25]

Biochemical parameters

Venous blood samples were collected in the morning at the same time (07:00 am to 09:00 am), after an overnight fast (8 to 10 h), and from the antecubital vein Participants were asked not to do any prolonged exercise during the

24 h prior to the exam Blood samples were analyzed for concentrations of fasting plasma glucose and lipid profile (total cholesterol, triglycerides, and high-density lipo-protein cholesterol (HDL-C)) using a routine colorimetric method (Biosystems BTS-303 Photometric, Barcelona,

Spain) High-sensitivity C-reactive protein (hs-CRP) was

quantified using a turbid metric test (SPINREACT, Spain),

and insulin levels were determined using an insulin

mi-croplate ELISA test (Monobind, USA) Samples were

processed and analyzed in the clinical laboratory of

bac-teriology school of the University of Santander - UDES

Homeostasis model assessment for insulin resistance

(IR) was calculated using the equation:

HOMA-IR = Fasting insulin (lU/ml) x Fasting glucose (mg/dl)/

405 [26]

Cardiovascular and metabolic risk definition

For this study, the cardiovascular and metabolic risk in

chil-dren and adolescents was defined according to a modified

version of the National Health and Nutrition Examination

Survey (NHANES) definition of metabolic syndrome

(MetS) [27] The considered parameters were: increased

waist circumference (≥75th percentile for age and sex of

study cohort), elevated triglycerides (≥110 mg/dl), low

HDL-C (≤40 mg/dl), elevated systolic blood pressure and/

or diastolic blood pressure (≥90 percentile for age, sex and

height), and elevated fasting plasma glucose (≥100 mg/dl)

MetS was defined by the presence of 3 or more of the

above criteria [27] Although the NHANES definition was

not intended to be applied to children below 12 years of

age, for the purposes of this study to enable comparisons to

be made and as cardiovascular and metabolic alterations

can be present in children from their earliest years of

life [2, 3], we have defined the individual risk

compo-nents of MetS across the complete sample of children

aged between 8 to 14 years Moreover, a value of≥2.6 in

HOMA-IR was considered to indicate insulin resistance

[28], and values of hs-CRP≥0.55 mg/dl (75th

percentile in our study sample) were considered as low-grade systemic

inflammation

Statistical analysis

Descriptive statistics were computed for variables of

interest, and included mean values and standard

deviations of continuous variables and absolute and

relative frequencies of categorical factors Normality

of distribution was checked for continuous variables using

the Shapiro-Wilk test and by graphical methods

Student’s t-test and Mann-Whitney test were used to

assess potential differences in continuous variables We

tested for differences in categorical variables using the

Pearson’s chi-squared test (Chi2

) Correlations between cardio-metabolic risk factors and anthropometric

vari-ables were evaluated using the Pearson’s correlation or

Spearman’s correlation coefficient, according to normality of

distributions Multiple linear regression analysis was applied

to further examine these associations

For selection of the cut-off points of NC that could

identify MetS, insulin resistance and low-grade systemic

inflammation according to gender, analyzes were made using the ROC (receiver operating characteristic) curves The statistical significance of each analysis was verified

by the area under the ROC curve (AUCs) and by 95 % confidence intervals (95 % CI´s) The maximum values

of the Youden’s index [29] were used as a criterion for selecting the optimum cut-off points All statistical analyzes were carried out using Stata statistical soft-ware, release 11.0 (Stata Corporation, College Station,

TX, USA) Ap < 0.05 was considered statistically significant Results

Descriptive statistics

As it has been previously reported [30, 31], a total of 669 children and adolescents were recruited during the cross-sectional component of the ACFIES study, of which 351 (52.5 %) were boys The overall mean age was 11.5 ± 1.1 years Demographic, anthropometric and metabolic characteristics of the study population by sex are presented

in Table 1 Compared to the girls, mean systolic blood pres-sure, waist circumference, WHR, WHtR, NC and %BF-Skinfold were significantly higher, while height, %BF-BIA, triglycerides, insulin and HOMA-IR were significantly lower in boys Among our study population, 85 (12.9 %) were overweight and 65 (9.8 %) were obese There were no statistically significant differences in weight status and BMI between both genders Sex-specific prevalences of MetS and its individual abnormalities, insulin resistance and low-grade systemic inflammation were also estimated (Fig 1), and statistical differences were not found

Correlation between anthropometric indexes and cardio-metabolic risk factors

Correlations of anthropometric indexes and cardio-metabolic risk factors are presented in Table 2 for the total sample and by gender Z-score BMI was positively corre-lated with triglycerides, systolic and diastolic blood pres-sure, hs-CRP, insulin and HOMA-IR in both genders, and inversely correlated with HDL-C only in boys Z-score WC was positively correlated with triglycerides, systolic and dia-stolic blood pressure, insulin and HOMA-IR in both gen-ders, with fasting plasma glucose and hs-CRP only in girls, and inversely correlated with HDL-C only in boys WHR was positively correlated only with triglycerides in both genders, with diastolic blood pressure, insulin and

HOMA-IR only in boys, and with hs-CRP only in girls WHtR was positively correlated with triglycerides, systolic and diastolic blood pressure, insulin and HOMA-IR in both genders, and with hs-CRP only in girls %BF-BIA was positively correlated with triglycerides, systolic and diastolic blood pressure, insulin and HOMA-IR in both genders, with hs-CRP only in girls, and inversely correlated with HDL-C only in girls %BF-Skinfold was positively correlated with systolic and diastolic blood pressure, hs-CRP, insulin and

HOMA-IR in both genders, with triglycerides only in

boys, and inversely correlated with HDL-C in both

genders NC was positively correlated with fasting

plasma glucose, systolic and diastolic blood pressure,

hs-CRP, insulin and HOMA-IR in both genders, with

triglycerides only in boys, and inversely correlated with

HDL-C in both genders

Multiple linear regression analysis between anthropometric indexes and cardio-metabolic risk factors

Table 3 illustrates the results of the multivariate regres-sion analysis conducted using separately each CVD risk factor as the dependent variable and controlling for age, gender and Tanner stage Fating plasma glucose was significantly associated only with NC, and HDL-C

Table 1 Demographic, anthropometric and metabolic data

Total

Anthropometric measures a

Biochemical measurements a

Weight status ( n - %) d

Tanner stage ( n - %) e

SBP systolic blood pressure, DBP diastolic blood pressure, BMI body mass index, WC waist circumference, WHR waist-to-hip ratio, WHtR waist-to-height ratio,

NC neck circumference, %BF-BIA body fat percentage – bioelectrical impedance analysis, %BF-Skinfold body fat percentage – skinfolds, FPG fasting plasma glucose,

TC total cholesterol, HDL-C high-density lipoprotein cholesterol, TG triglycerides, hs-CRP high sensitivity C-reactive protein

a

Data are presented as mean ± standard deviation for continuous variables b

Mann-Whitney test p < 0.05 c

Pearson ’s chi-squared test (Chi 2

) p <0.05

d

data missing for 11 participants

e

data missing for 15 participants

was associated with waist circumference and NC In

contrast, triglycerides, hs-CRP, insulin and HOMA-IR

were significantly associated with all the

anthropomet-ric indices, whereas systolic and diastolic blood

pres-sures were associated with all the anthropometric

indices, except WHR

Neck circumference cut-off points to identify MetS, insulin resistance and low-grade systemic inflammation according

to gender

The cut-off points and respective sensitivity and specificity values, the AUCs and the Youden’s index of NC for the identification of MetS, insulin resistance and low-grade

Fig 1 Prevalence of metabolic syndrome and its components, insulin resistance and low-grade systemic inflammation among study population Data are presented as relative frequencies with 95 % confidence intervals represented by vertical bars Significant differences between girls and boys (Pearson ’s chi-squared test (Chi 2

)) FPG: fasting plasma glucose; HDL-C: high-density lipoprotein cholesterol; TG: triglycerides; SBP: systolic blood pressure; DBP: diastolic blood pressure; WC: waist circumference; hs-CRP: high sensitivity C-reactive protein

Table 2 Correlations between cardiometabolic risk factors and anthropometric measurements according to gender

*Spearman ’s correlation coefficient p < 0.05 **Spearman’s correlation coefficient p < 0.001

BMI body mass index, WC waist circumference, %BF-BIA body fat percentage – bioelectrical impedance analysis, %BF-Skinfold body fat percentage – skinfolds, FPG fasting plasma glucose, HDL-C high-density lipoprotein cholesterol, TG triglycerides, SBP systolic blood pressure, DBP diastolic blood pressure, hs-CRP high sensitivity

systemic inflammation according to gender are shown in

Table 4 NC cut-off values for MetS were calculated to be

28.5 cm (95 % CI, 0.68 – 0.78) in girls and 29 cm (95 %

CI, 0.68– 0.78) in boys, 29.3 cm (95 % CI, 0.49 – 0.60) in

girls and 29.2 (95 % CI, 0.47– 0.58) in boys for detecting

low-grade systemic inflammation, and 29 cm (95 % CI,

0.51– 0.62) in girls and 30 cm (95 % CI, 0.49 – 0.59) in

boys for identifying insulin resistance (Table 5)

Discussion

We found that NC was associated with all the assessed

cardio-metabolic risk factors similar to that observed for

waist circumference, which was associated with all the

cardio-metabolic risk factors except fasting plasma glucose

The association for HDL-C was more robust for NC than

for waist circumference The other anthropometric indices were not associated neither with fasting plasma glucose nor HDL-C, and WHR was also not associated with systolic and diastolic blood pressure Interestingly, similar NC cut-off points for identifying children at elevated risk of MetS, insulin resistance and low-grade systemic inflammation were obtained by gender (28.5 to 29.3 cm in girls and 29 to

30 cm in boys), making it a simple marker of metabolic risk Therefore, NC is a measure that potentially might be implemented in situations where equipment availability or cultural issues limit the use of the traditional anthropomet-ric measures

Moreover, it should be noted that in cases wherein sig-nificant associations were found, most of the anthropomet-ric measures were similar to each other in the strength of

Table 3 Multiple linear regression analysis, using each cardiometabolic risk factor as the dependent variable

After controlling for age, gender and Tanner stage

FPG fasting plasma glucose, HDL-C high-density lipoprotein cholesterol, TG triglycerides, SBP systolic blood pressure, DBP diastolic blood pressure, hs-CRP high sensitivity C-reactive protein, BMI body mass index, WC waist circumference, WHR waist-to-hip ratio, WHtR waist-to-height ratio, %BF-BIA body fat percentage – bioelectrical impedance analysis, %BF-Skinfold body fat percentage – skinfolds, NC neck circumference

these associations Thus, our results confirm the value of a

complete anthropometric assessment in the identification

of cardiovascular and metabolic risk factors in children

Adiposity is widely accepted to play a key role in the

pathogenesis of cardiovascular and metabolic diseases in

children [3–5, 32] So, it is important the identification

of overweight children with cardio-metabolic risk factors

in whom counseling and treatment must be provided in

a timely manner The determination of biochemical

vari-ables is costly, making impractical its use as a screening

tool, particularly in low-middle income countries with

lower resources Thus, the present findings showing that

NC, which only requires a tape measure, is effective,

simple, easy-to-use and inexpensive anthropometric

measurement to identify children and adolescents with

cardio-metabolic risk constitute an important

contribu-tion from a public health perspective

However, previous studies [20, 21] have assessed the

association between NC and cardio-metabolic risk in

children, our study has the strength of having the largest

pediatric population sample to date Moreover, the re-sults showed for the first time, an association between high NC and abnormal values of fasting plasma glucose and low-grade systemic inflammation These results sup-port the proposal of an increased cardio-metabolic risk

in our population at lower levels of adiposity [33–35] Although NC is an emerging marker of cardio-metabolic risk in children, it has been demonstrated as a good pre-dictor of cardiovascular disease in adults with different con-ditions such as MetS, obstructive sleep apnea and fatty liver disease [15, 19, 36–39]

BMI has been the accepted standard measure of over-weight and obesity for children two years of age and older [40] However, some studies have suggested that BMI is not a good indicator of cardio-metabolic risk [7, 8, 41] In our current study BMI was associated with most of the cardio-metabolic risk factors assessed, confirming that despite its apparent limitations, in children BMI is non in-ferior to measures that assess body composition and dif-ferentiate fat and lean mass, such as BIA or skinfolds [42]

We found that associations between BIA and skinfolds and cardio-metabolic risk factors were similar to that of the anthropometric indices; but, in contrast to NC, neither

of these measures was associated with fasting plasma glu-cose and HDL-C Moreover, it is notable that despite identical statistical associations with cardio-metabolic risk

of these two field measures of body composition, the mean values were lower for BIA in boys and girls and

%BF-BIA was significantly higher in girls than boys, while the reverse was the case for %BF-Skinfolds Therefore, it is not clear which of these two estimates of %BF is more accurate or whether it is appropriate to calculate them using predictive equations validated in different populations Fat distribution is also recognized as an important de-terminant of metabolic risk [43] and those anthropomet-ric measures such as waist circumference, WHR and WHtR are good indicators of visceral adipose tissue and therefore good predictors of cardiovascular risk [44–46]

Table 4 Neck circumference cut-offs points to identify metabolic syndrome, low-grade systemic inflammation and insulin resistance

in study sample according to gender

Metabolic Syndrome

Low-grade systemic inflammation

Insulin resistance

Receiver operating characteristic (ROC) analyzes Youden’s index = Sensitivity + Specificity – 1

Table 5 Advantages and limitations in pediatric population of

anthropometrics measurements to identify metabolic alterations

(-) Not correlation; (+) Correlation in girls or boys; (++) Correlation in both girls

and boys

FPG fasting plasma glucose, HDL-C high-density lipoprotein cholesterol, TG

triglycerides, SBP systolic blood pressure, DBP diastolic blood pressure, hs-CRP

high sensitivity C-reactive protein, BMI body mass index, WC waist circumference,

WHR waist-to-hip ratio, WHtR waist-to-height ratio, %BF-BIA body fat percentage –

bioelectrical impedance analysis, %BF-Skinfold body fat percentage – skinfolds,

NC neck circumference

In the present study, all these anthropometric indexes

showed acceptable correlations with the cardio-metabolic

risk factors, although none were superior to NC Hence,

in agreement with previous studies, we can also suggest

the use of waist circumference, WHR and WHtR as an

optional adiposity indexes in relation to the cardiovascular

and metabolic health risk

Our study should be interpreted in light of its limitations

First, is a cross-sectional study; therefore, the association

with cardiovascular and metabolic disease outcomes could

not be established Second, as pubertal growth and

develop-ment is characterized by changes in metabolic traits that

characterize the MetS [47], we suggest further studies with

larger sample sizes, in which the cut-off points would be

defined by pubertal development Third, we defined the

cardio-metabolic risk using a modified NHANES definition

of MetS, which we considered as the most applicable in the

clinical practice based on the simplicity of its diagnostic

cri-teria, however it should be mentioned that the appropriate

risk factor cut-offs for children remain controversial, and

therefore further studies to define thresholds for

abnormal-ities of the metabolic components should be conducted

Fourth, our study was specifically conducted in a pediatric

Latin American population It has been proposed that fetal

programming associated to maternal undernutrition, which

prevalence still is high in Latin America, could affect the

body composition and the utility of different

anthropomet-rics measurements [35] Hence, we believe that additional

studies should be performed testing whether the proposed

cut-offs points for NC are truly applicable in other

popula-tions and regions of the world

Conclusions

We evaluated the association between several

cardio-metabolic risk factors and NC, a novel marker of risk,

and compared this with classic anthropometric measures

and indexes such as BMI and WHR and with field

mea-sures of body composition While all of the

anthropo-metric measures and indexes we assessed showed some

associations with cardio-metabolic risk factors, including

insulin resistance and low-grade systemic inflammation,

we found that NC was the most consistent and robust

marker Further longitudinal studies in representative

populations are required to confirm these findings and

to establish NC as a basic criterion in the diagnosis of

cardio-metabolic risk factors

Competing interests

The ACFIES study is partially funded by the MAPFRE Foundation and the

mayor of Bucaramanga, Colombia The authors declare that they have no

competing interests.

Authors ’ contributions

PLJ, DGA and DDC conceived the project DGA, DDC, CLL and SSC carried out

experiments DGA and PAC analyzed data All authors were involved in writing

the paper and had final approval of the submitted and published versions.

Acknowledgements The authors would like to thank principals and teachers of the school

“INEM - Custodio Garcia Rovira”, and schools of medicine, physiotherapy, nursing and bacteriology at the University of Santander - UDES for their assistance with the study.

Author details

1

Dirección de Investigaciones, Fundación Oftalmológica de Santander -FOSCAL, Floridablanca, Colombia 2 Instituto MASIRA, Facultad de la Ciencias

de la Salud, Universidad de Santander - UDES, Bucaramanga, Colombia.

3 Departamento de Endocrinología, Escuela de Medicina, Universidad de Santiago de Compostela, Santiago de Compostela, España.4Escuela de Medicina, Universidad Autónoma de Bucaramanga – UNAB, Bucaramanga, Colombia 5 Fundación Oftalmológica de Santander - FOSCAL, Calle 155A N.

23 –09, El Bosque, Floridablanca, Santander, Colombia.

Received: 3 July 2015 Accepted: 29 February 2016

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